1. Field of the Invention
This invention relates generally to semiconductor device manufacturing and, more particularly, to a method and apparatus for monitoring tool health.
2. Description of the Related Art
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a lot of wafers using a variety of processing tools, including photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal processing tools, implantation tools, etc. The technologies underlying semiconductor processing tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the processing tools that are currently commercially available suffer certain deficiencies. In particular, such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender nonoptimal control of critical processing parameters, such as throughput, accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
One technique for improving the operation of a semiconductor processing line includes using a factory wide control system to automatically control the operation of the various processing tools. The manufacturing tools communicate with a manufacturing framework or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface that facilitates communications between the manufacturing tool and the manufacturing framework. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
Various tools in the processing line are controlled in accordance with performance models to reduce processing variation. Commonly controlled tools include photolithography steppers, polishing tools, etching tools, annealing tools, and deposition tools. Pre-processing and/or post-processing metrology data is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology information to attempt to achieve post-polishing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate to increased profitability.
One technique for monitoring the operation of a particular tool involves employing a multivariate tool health model adapted to predict the expected operating parameters of the tool during the processing of wafers in the tool. If the actual tool parameters are close to the predicted tool parameters, the tool is said to have a high health rating (i.e., the tool is operating as expected). As the gap between the expected tool parameters and the actual tool parameters widens, the tool health rating decreases. If the tool health rating falls below a predetermined threshold, a maintenance procedure may be performed to troubleshoot or repair the tool. If the tool health is sufficiently low, the wafers processed by the tool in the degraded condition may be flagged as suspect or reworked.
Typically, the model used to predict the operating parameters of the tool, thereby measuring the health of the tool, is based on the particular tool and the base operating recipe employed by the tool for processing the wafers. Hence, each tool has a separate tool health model for each of the base operating recipes run on the tool. An exemplary tool health monitor software application is ModelWare(trademark) offered by Triant, Inc. of Nanaimo, British Columbia, Canada Vancouver, Canada.
Commonly, a tool undergoes periodic preventative maintenance procedures or calibrations to keep the tool in optimum operating condition. For example, polishing tools include polishing pads that are periodically conditioned or replaced. Etch tools, annealing tools, and deposition tools are periodically cleaned using both in-situ cleaning processes or complete disassembly cleaning processes. Steppers are periodically calibrated to maintain alignment accuracy and exposure dose consistency. The discrete maintenance activities, collectively referred to as tool events, often cause step changes in the processing characteristics of the tool.
Other types of events experienced by the tool cause incremental changes to the operating characteristics of the tool. For example, certain parameters in an operating recipe of the tool may be controlled by a process controller based on metrology data to reduce variation in an output characteristic of the wafers processed in the tool (e.g., thickness of a process layer deposited in deposition tool or critical dimension of a photoresist pattern exposed in a stepper). Although the actual operating recipe used by the tool is periodically changed by the process controller, the base operating recipe, in the eyes of the manufacturing control system, remains the same.
Yet another source of variation in the operating characteristics of a tool results from degradation of consumable items used in the tool. For example, polishing pads degrade over time in a polishing tool, the intensity of a lamp used to expose photoresist layers in a stepper degrades over time, and byproducts build up in a deposition chamber over time increasing the amount of particle contamination.
The effectiveness of the multivariate fault detection technique described above depends on how accurately the tool model matches the actual operating conditions of the tool. If the tool health model does not accurately reflect the actual operating conditions of the tool, the tool parameters predicted by the model will diverge from the actual tool parameters measured during the processing run. A degraded tool health measurement may be indicated based on a failure of the tool health monitor as opposed to an actual degraded condition of the tool. As a result, the tolerances used by the tool health monitor for diagnosing a degraded tool condition must be set such that the likelihood of giving a false degraded health indication is reduced. Necessarily, this reduces the sensitivity of the tool health monitor and causes an increase in the number of tool health problems that are undiagnosed.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.
One aspect of the present invention is seen in a method for monitoring health of a tool. The method includes receiving at least one tool parameter related to the processing of a workpiece in a tool; receiving a model selection trigger; selecting a tool health model based on the model selection trigger; generating at least one predicted tool parameter based on the selected tool health model; and generating a tool health rating for the tool based on a comparison between the measured tool parameter and the predicted tool parameter.
Another aspect of the present invention is seen in a tool health monitor including a library of tool health models, a model selector, and a fault detection and classification unit. The model selector is adapted to receive a model selection trigger and select a tool health model based on the model selection trigger. The fault detection and classification unit is adapted to receive at least one tool parameter related to the processing of a workpiece in a tool, generate at least one predicted tool parameter based on the selected tool health model, and generate a tool health rating for the tool based on a comparison between the received tool parameter and the predicted tool parameter.